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PyTorch code for our ECCV 2018 paper "Image Super-Resolution Using Very Deep Residual Channel Attention Networks"
The main features of yulunzhang/rcan are: Computer Vision Research, Image Transformation.
Projects with overlapping indexed features include: yulunzhang/rdn — Torch code for our CVPR 2018 paper "Residual Dense Network for Image Super-Resolution" (Spotlight). richzhang/perceptualsimilarity — PerceptualSimilarity is a deep learning framework designed to quantify and evaluate the perceptual distance between… dmitryulyanov/deep-image-prior — This project is an unsupervised image restoration tool that uses a convolutional neural network as a structural prior… zalandoresearch/fashion-mnist — This project is a computer vision benchmark and image classification dataset used to measure and compare the accuracy… akanazawa/cmr — Angjoo Kanazawa \ , Shubham Tulsiani \ , Alexei A. Efros, Jitendra Malik. agrimgupta92/sgan — Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks", Gupta et al, CVPR 2018.
PerceptualSimilarity is a deep learning framework designed to quantify and evaluate the perceptual distance between images. It provides a system for measuring how similar two images or image patches appear to human vision by using deep feature representations instead of pixel-wise differences. The project implements a differentiable distance metric that functions as a loss function, allowing image pixels to be optimized via backpropagation to reach a target visual appearance. It includes a trainable linear layer that can be applied to frozen deep features to learn weighted distance metrics al
Torch code for our CVPR 2018 paper "Residual Dense Network for Image Super-Resolution" (Spotlight)
This project is an unsupervised image restoration tool that uses a convolutional neural network as a structural prior to reconstruct images from noisy or incomplete data. It functions as a neural network image prior, utilizing the inherent biases of the network architecture to restore pixels without the need for a pre-trained dataset or external learning. The system performs zero-shot image restoration by treating the network architecture itself as a regularization term. It uses a randomly initialized encoder-decoder structure and iterative gradient descent to minimize pixel-wise loss, recove
This project is a computer vision benchmark and image classification dataset used to measure and compare the accuracy of machine learning models. It provides a standardized collection of labeled fashion product images and training data formatted to be compatible with the MNIST dataset structure. The dataset consists of fixed-dimension grayscale images and label-based category mappings, stored in a binary format. It includes pre-split training and testing sets and a static distribution to ensure consistent cross-model benchmarking. The repository supports image classification benchmarking and